Hi, my name is João F. Henriques. (Sounds a bit like “joo-au” in English.) I like to work in the convex hull of machine learning, deep learning and computer vision. Perhaps my most well-known works are on visual tracking, but I have many favourite topics: robotics, AI safety, 3D (NeRFs/splats), and optimisation.
My talented DPhil students:
Marian Longa · Tim Franzmeyer · Dominik Kloepfer · Yash Bhalgat · Shivani Mall · Lorenza Prospero · Mark Eid
(Graduated: Xu Ji · Mandela Patrick · Shu Ishida · Andreea Oncescu)
Preprints
Sneak peek at upcoming research
S. Szymanowicz, E. Insafutdinov, C. Zheng, D. Campbell, J. F. Henriques, C. Rupprecht, A. Vedaldi
arXiv, 2024
PDF Code arXivY. Bhalgat, V. Tschernezki, I. Laina, J. F. Henriques, A. Vedaldi, A. Zisserman
arXiv, 2024
PDF arXivS. Ishida, J. F. Henriques
arXiv, 2024
PDF Code arXivM. C. Eid, P. Yeung, M. K. Wyburd, J. F. Henriques, A. I. L. Namburete
arXiv, 2024
PDF arXivL. Prospero, A. Hamdi, J. F. Henriques, C. Rupprecht
arXiv, 2024
PDF Code arXivResearch
Publications, talks and source-code
Filter by topic
All topics Computer vision Reinforcement learning Robotics 3D vision Multimodal and video ML optimization Meta-learning Object tracking Language modeling Audio Transformation invariance
M. Longa, J. F. Henriques
NeurIPS, 2024
PDF arXivInterpretable Representation Learning from Videos using Nonlinear Priors
M. Longa, J. F. Henriques
BMVC, 2024
Y. Liang, K. Ellis, J. F. Henriques
CVPR, 2024
PDF arXivY. Bhalgat, I. Laina, J. F. Henriques, A. Zisserman, A. Vedaldi
ECCV, 2024
PDF arXivT. Franzmeyer, E. Elkind, P. Torr, J. Foerster, J. F. Henriques
ICLR, 2024
PDF Code arXivIllusory Attacks: Information-theoretic detectability matters in adversarial attacks
T. Franzmeyer, S. McAleer, J. F. Henriques, J. Foerster, P. Torr, A. Bibi, C. Schroeder de Witt
ICLR, 2024
T. Franzmeyer, A. Shtedritski, S. Albanie, P. Torr, J. F. Henriques, J. N. Foerster
ACL, 2024
PDF Code arXivD. Kloepfer, D. Campbell, J. F. Henriques
3DV, 2024
PDF arXivA Sound Approach: Using Large Language Models to generate audio descriptions for egocentric text-audio retrieval
A. Oncescu, J. F. Henriques, A. Zisserman, S. Albanie, A. S. Koepke
ICASSP, 2024
A. Oncescu, J. F. Henriques, A. S. Koepke
ACM International Conference on Multimedia, 2024
PDF arXivY. Xia, L. Shi, Z. Ding, J. F. Henriques, D. Cremers
CVPR, 2024
PDF arXivS. Ishida, G. Corrado, G. Fedoseev, H. Yeo, L. Russell, J. Shotton, J. F. Henriques, A. Hu
ICLR Workshop on LLM Agents, 2024
PDF Blog Code arXivNeural Fields for Co-Reconstructing 3D Objects from Incidental 2D Data
D. Campbell, E. Insafutdinov, J. F. Henriques, A. Vedaldi
CVPR Workshop on Neural Rendering Intelligence, 2024
PDFY. Bhalgat, I. Laina, J. F. Henriques, A. Zisserman, A. Vedaldi
NeurIPS, 2023
PDF arXivD. Kloepfer, D. Campbell, J. F. Henriques
ICCV, 2023
PDF AppendixN. Nayal, M. Yavuz, J. F. Henriques, F. Güney
ICCV, 2023
PDF Appendix arXivY. Xia, M. Gladkova, R. Wang, Q. Li, U. Stilla, J. F. Henriques, D. Cremers
ICCV, 2023
PDF Appendix arXivY. Bhalgat, J. F. Henriques, A. Zisserman
CVPR, 2023
PDF arXivC. Oncescu, J. Valmadre, J. F. Henriques
Tiny Papers at ICLR, 2023
PDFT. Franzmeyer, P. Torr, J. F. Henriques
NeurIPS, 2022
PDF arXiv
SNeS: Learning Probably Symmetric Neural Surfaces from Incomplete Data
E. Insafutdinov, D. Campbell, J. F. Henriques, A. Vedaldi
ECCV, 2022
We augment neural radiance fields to render views of partially-symmetric objects that are not seen in the data, such as when seeing a car from just one side. Since shadows and reflections break object symmetry, in the process we decompose scenes into geometry, light and material properties.
Towards real-world navigation with deep differentiable planners
S. Ishida, J. F. Henriques
CVPR, 2022
We train robot agents to explore and seek semantic goals, without hazardous trial-and-error, by using only safe demonstrations. We achieve this by extending and improving on Value Iteration Networks, enabling robots to cope even with mazes with a high branching factor.
Audio retrieval with natural language queries: A benchmark study
A. S. Koepke, A. Oncescu, J. Henriques, Z. Akata, S. Albanie
IEEE Transactions on Multimedia, 2022
Illusionary Attacks on Sequential Decision Makers and Countermeasures
T. Franzmeyer, J. F. Henriques, J. N. Foerster, P. H. Torr, A. Bibi, C. S. de Witt
arXiv, 2022
PDF arXivKeeping your eye on the ball: Trajectory attention in video transformers
M. Patrick, D. Campbell, Y. M. Asano, I. M. F. Metze, C. Feichtenhofer, A. Vedaldi, J. F. Henriques
NeurIPS, 2021 (oral presentation)
We improve video transformers (e.g. for action recognition) by encouraging attention pooling over motion paths. We also reduce the quadratic computational complexity of attention to linear, with a rigorous probabilistic approximation based on orthogonal prototypes.
Multi-modal self-supervision from generalized data transformations
M. Patrick, Y. M. Asano, P. Kuznetsova, R. Fong, J. F. Henriques, G. Zweig, A. Vedaldi
ICCV, 2021
Most contrastive self-supervised methods learn representations that are distinctive to individual examples, and invariant to several other factors. We propose a framework to systematically evaluate valid combinations of distinctive and invariant factors, yielding superior performance in many multi-modal learning tasks.
M. Patrick, Y. M. Asano, P. Huang, I. Misra, F. Metze, J. F. Henriques, A. Vedaldi
ICCV, 2021
PDF Code arXivJ. Jiao, J. F. Henriques
BMVC, 2021
PDF
Support-set bottlenecks for video-text representation learning
M. Patrick, P. Huang, Y. Asano, F. Metze, A. G. Hauptmann, J. F. Henriques, A. Vedaldi
ICLR, 2021
We investigate noise-contrastive learning of video-text neural networks. We find that learning to reconstruct video captions with video retrieval as a representational bottleneck yields better semantic representations.
Audio retrieval with natural language queries
A. Oncescu, A. S. Koepke, J. F. Henriques, Z. Akata, S. Albanie
Interspeech, 2021 (nominated for best student paper award)
Creating a content-based audio search engine. Similar to Google Images, but for audio instead.
X. Ji, J. Henriques, T. Tuytelaars, A. Vedaldi
NeurIPS Workshops, 2020
Avoiding catastrophic forgetting with context-sensitive generative recall, inspired by biological memory.
PDF arXivB. Davidson, M. S. Alvi, J. F. Henriques
ECCV, 2020
PDFP. Martins, J. F. Henriques, J. Batista
IJCV, 2020
PDF
P. Martins, J. F. Henriques, R. Caseiro, J. Batista
TPAMI, 2016
PDF VideoLearning feed-forward one-shot learners
L. Bertinetto, J. F. Henriques, J. Valmadre, P. H. S. Torr, A. Vedaldi
NeurIPS, 2016
Early work on meta-learning for one-shot learning, where a deep network predicts the parameters of another network, given a few examples of a classification task.
R. Caseiro, P. Martins, J. F. Henriques, J. Batista
CVPR, 2015
PDF
P. Martins, R. Caseiro, J. F. Henriques, J. Batista
ICIP, 2014 (top 10% of accepted papers)
PDF Video arXivR. Caseiro, P. Martins, J. F. Henriques, J. Carreira, J. Batista
CVPR, 2013 (oral presentation)
PDFR. Caseiro, J. F. Henriques, P. Martins, J. Batista
ECCV, 2012
PDFP. Martins, R. Caseiro, J. F. Henriques, J. Batista
ECCV, 2012
PDF VideoP. Martins, R. Caseiro, J. F. Henriques, J. Batista
BMVC, 2012 (oral presentation)
PDF VideoR. Caseiro, P. Martins, J. F. Henriques, J. Batista
Pattern Recognition, 2012
PDF
R. Caseiro, J. F. Henriques, P. Martins, J. Batista
ICCV, 2011
PDFJ. F. Henriques, R. Caseiro, J. Batista
ICIP, 2010
PDFR. Caseiro, J. F. Henriques, J. Batista
ICIP, 2010
PDFMore
Research-related
Workshops on Preregistration
An alternative publication model for machine learning research
Preregistration separates the generation and confirmation of hypotheses:
Come up with an exciting research question
Write a paper proposal without confirmatory experiments
After the paper is accepted, run the experiments and report your results
There are several advantages in this model: 1) A healthy mix of positive and negative results; 2) Reasonable ideas that don’t work still get published, avoiding wasteful replications; 3) Papers are evaluated on the basis of scientific interest, not whether they achieve the best results; 4) It is easier to plan research; and 5) results are statistically stronger. Check the pages below for more information, including talks and preregistered machine learning papers.
OverBoard
A pure Python dashboard for monitoring deep learning experiments
OverBoard is a lightweight yet powerful dashboard to monitor your experiments. It includes:
A table of hyper-parameters with Python-syntax filtering
Multiple views of the same data (i.e. custom X/Y axes)
Hyper-parameter visualisation (i.e. bubble plots)
Percentile intervals for multiple runs (i.e. shaded plots)
Custom visualisations (tensors and any custom plot with familiar MatPlotLib syntax)
Fast client-side rendering (the training code is kept lightweight)
You can install it with: pip install overboard
Its only dependences are PyQtGraph (conda install pyqt pyqtgraph -c anaconda) and Python 3.
Fun
Not mutually-exclusive with research
S. Albanie, J. Thewlis, S. Ehrhardt, J. F. Henriques
Narrowly missed SIGBOVIK, 2019
The most end-to-end network ever proposed, and a sunnier alternative to cloud computing. Narrowly missing the deadline for SIGBOVIK 2019, received the Most timely paper
award at SIGBOVIK 2020.
Stopping GAN violence: Generative unadversarial networks
S. Albanie, S. Ehrhardt, J. F. Henriques
SIGBOVIK, 2017
An attempt to end the madness of pitting network-against-network (GAN training). This paper achieved moderate success on social media, which meant that all subsequent papers were doomed to obscurity (but that didn't stop us).
Surprisingly, there is an entirely serious paper that experiments with generative unadversarial training
and credits our joke paper as the inspiration! (With full knowledge that it is not to be taken seriously of course.) Mission accomplished.